





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__			:					int       						=						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Dict       						=						['AlbertTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Optional[Any]       						=						['AlbertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Optional[Any]       						=						[
        'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AlbertForMaskedLM',
        'AlbertForMultipleChoice',
        'AlbertForPreTraining',
        'AlbertForQuestionAnswering',
        'AlbertForSequenceClassification',
        'AlbertForTokenClassification',
        'AlbertModel',
        'AlbertPreTrainedModel',
        'load_tf_weights_in_albert',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Optional[int]       						=						[
        'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAlbertForMaskedLM',
        'TFAlbertForMultipleChoice',
        'TFAlbertForPreTraining',
        'TFAlbertForQuestionAnswering',
        'TFAlbertForSequenceClassification',
        'TFAlbertForTokenClassification',
        'TFAlbertMainLayer',
        'TFAlbertModel',
        'TFAlbertPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Any       						=						[
        'FlaxAlbertForMaskedLM',
        'FlaxAlbertForMultipleChoice',
        'FlaxAlbertForPreTraining',
        'FlaxAlbertForQuestionAnswering',
        'FlaxAlbertForSequenceClassification',
        'FlaxAlbertForTokenClassification',
        'FlaxAlbertModel',
        'FlaxAlbertPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_albert import AlbertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_albert_fast import AlbertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_albert import (
            ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForPreTraining,
            AlbertForQuestionAnswering,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertModel,
            AlbertPreTrainedModel,
            load_tf_weights_in_albert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_albert import (
            TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAlbertForMaskedLM,
            TFAlbertForMultipleChoice,
            TFAlbertForPreTraining,
            TFAlbertForQuestionAnswering,
            TFAlbertForSequenceClassification,
            TFAlbertForTokenClassification,
            TFAlbertMainLayer,
            TFAlbertModel,
            TFAlbertPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_albert import (
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForPreTraining,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertModel,
            FlaxAlbertPreTrainedModel,
        )
else:
    import sys

    a__			:					Dict       						=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




def a__       (			A_=28123   ):






  '''simple docstring'''





  __magic_name__								=			[1] * (limit + 1)

  for i in range(2,	int(limit**0.5   ) + 1   ):
    sum_divs[i * i] += i
    for k in range(i + 1,	limit // i + 1   ):
      sum_divs[k * i] += k + i

  __magic_name__								=			set()
  __magic_name__								=			0

  for n in range(1,	limit + 1   ):
    if sum_divs[n] > n:
      abundants.add(A_   )

    if not any((n - a in abundants) for a in abundants   ):
      res += n

  return res


if __name__ == "__main__":
    print(solution())

